卫星遥感地表温度降尺度的光谱归一化指数法

doi:10.11947/j.AGCS.2017.20160196

测绘学报 ›› 2017, Vol. 46 ›› Issue (3): 353-361.doi: 10.11947/j.AGCS.2017.20160196

卫星遥感地表温度降尺度的光谱归一化指数法

李小军^1,2, 辛晓洲¹, 江涛³, 张海龙¹

1. 中国科学院遥感与数字地球研究所遥感科学国家重点实验室, 北京 100101;
2. 中国科学院大学, 北京 100049;
3. 山东科技大学测绘科学与工程学院, 山东青岛 266590

收稿日期:2016-04-27 修回日期:2017-01-20 出版日期:2017-03-20 发布日期:2017-04-11
通讯作者: 辛晓洲 E-mail:xin_xzh@163.com
作者简介:李小军(1992-),男,硕士生,研究方向为地表辐射与能量平衡遥感估算理论与方法。E-mail:kdxiaojun@126.com
基金资助:
国家自然科学基金（41371360）

Spatial Downscaling Research of Satellite Land Surface Temperature Based on Spectral Normalization Index

LI Xiaojun^1,2, XIN Xiaozhou¹, JIANG Tao³, ZHANG Hailong¹

1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, Beijing 100101, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. Geomatics College, Shangdong University of Science and Technology, Qingdao 266690, China

Received:2016-04-27 Revised:2017-01-20 Online:2017-03-20 Published:2017-04-11
Supported by:
The National Natural Science Foundation of China (No. 41371360)

摘要/Abstract

摘要： 针对卫星遥感技术监测地表温度（land surface temperature，LST）存在时空分辨率矛盾这一难题，以TsHARP温度降尺度算法为基础，根据地表覆盖类型的不同，分别选择与LST相关性更好的光谱指数（归一化植被指数，NDVI；归一化建造指数，NDBI；改进的归一化水体指数，MNDWI；增强型裸土指数，EBSI）提出了新的转换模型，并从定性和定量两个角度评价了TsHARP法和新模型的降尺度精度。结果表明：两种模型在提高LST空间分辨率的同时又能较好地保持MODIS LST影像热特征的空间分布格局，消除了原始1km影像中的马赛克效应，两种模型均能够达到较好的降尺度效果；全局尺度分析表明，不管是在降尺度结果的空间变异性还是精度方面，本文提出的模型（RMSE：1.635℃）均要优于TsHARP法（RMSE：2.736℃）；TsHARP法在水体、裸地和建筑用地这些低植被覆盖区表现出较差的降尺度结果，尤其对于裸地和建筑用地更为明显（|MBE|>3℃），新模型提高了低植被覆盖区地物的降尺度精度；不同季节的降尺度结果表明，两种模型都是夏、秋季的降尺度结果优于春、冬季，新模型的降尺度结果四季均好于TsHARP法，其中春、冬季的降尺度精度提升效果要优于夏、秋季。

关键词: MODIS, 降尺度, 地表温度, TsHARP算法, 地表覆盖

Abstract: Aiming at the problem that the spatial and temporal resolution of land surface temperature (LST) have the contradiction with each other, a new downscaling model was put forward, based on the TsHARP(an algorithm for sharpening thermal imagery) downscaling method, this research makes improvements by selecting the better correlation of spectral index(normalized difference vegetation index, NDVI; normalized difference build-up index, NDBI; modified normalized difference water index, MNDWI; enhanced bare soil index, EBSI) with LST, i.e., replaces the original NDVI with new spectral index according to the different surface land-cover types, to assess the accuracy of each downscaling method based on qualitative and quantitative analysis with synchronous Landsat 8 TIRS LST data. The results show that both models could effectively enhance the spatial resolution while simultaneously preserving the characteristics and spatial distribution of the original 1 km MODIS LST image, and also eliminate the “mosaic” effect in the original 1 km image, both models were proved to be effective and applicable in our study area; global scale analysis shows that the new model (RMSE:1.635℃) is better than the TsHARP method (RMSE:2.736℃) in terms of the spatial variability and accuracy of the results; the different land-cover types of downscaling statistical analysis shows that the TsHARP method has poor downscaling results in the low vegetation coverage area, especially for the bare land and building-up area(|MBE|>3℃), the new model has obvious advantages in the description of the low vegetation coverage area. Seasonal analysis shows that the downscaling results of two models in summer and autumn are superior to those in spring and winter, the new model downscaling results are better than the TsHARP method in the four seasons, in which the spring and winter downscaling improvement is better than summer and autumn.

Key words: MODIS, downscaling, land surface temperature, TsHARP method, land-cover

中图分类号:

P237

李小军, 辛晓洲, 江涛, 张海龙. 卫星遥感地表温度降尺度的光谱归一化指数法[J]. 测绘学报, 2017, 46(3): 353-361.

LI Xiaojun, XIN Xiaozhou, JIANG Tao, ZHANG Hailong. Spatial Downscaling Research of Satellite Land Surface Temperature Based on Spectral Normalization Index[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(3): 353-361.

参考文献

[1] KUSTAS W P, NORMAN J M, ANDERSON M C, et al. Estimating Subpixel Surface Temperatures and Energy Fluxes from the Vegetation Index-radiometric Temperature Relationship[J]. Remote Sensing of Environment, 2003, 85(4):429-440.
[2] GILLIES R R, CARLSON T N. Thermal Remote Sensing of Surface Soil Water Content with Partial Vegetation Cover for Incorporation into Climate Models[J]. Journal of Applied Meteorology, 1995, 34(4):745-756.
[3] WENG Qihao, LU Dengsheng. A Sub-pixel Analysis of Urbanization Effect on Land Surface Temperature and Its Interplay with Impervious Surface and Vegetation Coverage in Indianapolis, United States[J]. International Journal of Applied Earth Observation and Geoinformation, 2008, 10(1):68-83.
[4] HA W, GOWDA P H, HOWELL T A. A Review of Downscaling Methods for Remote Sensing-based Irrigation Management:Part I[J]. Irrigation Science, 2013, 31(4):831-850.
[5] 全金玲, 占文凤, 陈云浩, 等. 遥感地表温度降尺度方法比较——性能对比及适应性评价[J]. 遥感学报, 2013, 17(2):361-387. QUAN Jinling, ZHAN Wenfeng, CHEN Yunhao, et al. Downscaling Remotely Sensed Land Surface Temperature:A Comparison of Typical Methods[J]. Journal of Remote Sensing, 2013, 17(2):361-387.
[6] AGAM N, KUSTAS W P, ANDERSON M C, et al. A Vegetation Index Based Technique for Spatial Sharpening of Thermal Imagery[J]. Remote Sensing of Environment, 2007, 107(4):545-558.
[7] LIU Desheng, PU Ruiliang. Downscaling Thermal Infrared Radiance for Subpixel Land Surface Temperature Retrieval[J]. Sensors, 2008, 8(4):2695-2706.
[8] JEGANATHAN C, HAMM N A S, MUKHERJEE S, et al. Evaluating a Thermal Image Sharpening Model over a Mixed Agricultural Landscape in India[J]. International Journal of Applied Earth Observation and Geoinformation, 2011, 13(2):178-191.
[9] ZAKŠEK K, OŠTIR K. Downscaling Land Surface Temperature for Urban Heat Island Diurnal Cycle Analysis[J]. Remote Sensing of Environment, 2012(117):114-124.
[10] ZHU Shanyou, GUAN Huade, MILLINGTON A C, et al. Disaggregation of Land Surface Temperature over a Heterogeneous Urban and Surrounding Suburban Area:A Case Study in Shanghai, China[J]. International Journal of Remote Sensing, 2013, 34(5):1707-1723.
[11] YANG Guijun, PU Ruiliang, ZHAO Chunjiang, et al. Estimation of Subpixel Land Surface Temperature Using an Endmember Index Based Technique:A Case Examination on ASTER and MODIS Temperature Products over a Heterogeneous Area[J]. Remote Sensing of Environment, 2011, 115(5):1202-1219.
[12] KERAMITSOGLOU I, KIRANOUDIS C T, WENG Qihao. Downscaling Geostationary Land Surface Temperature Imagery for Urban Analysis[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(5):1253-1257.
[13] JIMÉNEZ-MUÑOZ J C, SOBRINO J A, SKOKOVIC' D, et al. Land Surface Temperature Retrieval Methods from Landsat-8 Thermal Infrared Sensor Data[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(10):1840-1843.
[14] 徐涵秋. 新型Landsat8卫星影像的反射率和地表温度反演[J]. 地球物理学报, 2015, 58(3):741-747. XU Hanqiu. Retrieval of the Reflectance and Land Surface Temperature of the Newly-launched Landsat 8 Satellite[J]. Chinese Journal of Geophysics, 2015, 58(3):741-747.
[15] 宋挺, 段峥, 刘军志, 等. Landsat 8数据地表温度反演算法对比[J]. 遥感学报, 2015, 19(3):451-464. SONG Ting, DUAN Zheng, LIU Junzhi, et al. Comparison of Four Algorithms to Retrieve Land Surface Temperature Using Landsat 8 Satellite[J]. Journal of Remote Sensing, 2015, 19(3):451-464.
[16] 王祎婷, 谢东辉, 李亚惠. 光谱指数趋势面的城市地表温度降尺度转换[J]. 遥感学报, 2014, 18(6):1169-1181. WANG Yiting, XIE Donghui, LI Yahui. Downscaling Remotely Sensed Land Surface Temperature over Urban Areas Using Trend Surface of Spectral Index[J]. Journal of Remote Sensing, 2014, 18(6):1169-1181.
[17] 李小军,江涛,辛晓洲,等.基于MODIS的地表温度空间降尺度方法[J].生态学杂志,2016,35(12):3443-3450. LI Xiaojun, JIANG Tao, XIN Xiaozhou, et al. Spatial Downscaling of Land Surface Temperature Based on MODIS Data[J]. Chinese Journal of Ecology, 2016, 35(12):3443-3450.
[18] 刘耀林, 赵翔, 马潇雅, 等. TM/ETM+影像大气校正产品质量评价方法研究[J]. 测绘学报, 2012, 41(4):549-555. LIU Yaolin, ZHAO Xiang, MA Xiaoya, et al. Research on Quality Assessment of Atmospheric Correction Products Retrieved from TM/ETM+ Imagery[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(4):549-555.
[19] 徐涵秋, 张铁军, 黄绍霖. Landsat-7 ETM+与ASTER建筑指数的定量比较[J]. 地理研究, 2013, 32(7):1336-1344. XU Hanqiu, ZHANG Tiejun, HUANG Shaolin. Quantitative Comparison of Landsat 7 and ASTER Multispectral Measurements for the NDBI and IBI[J]. Geographical Research, 2013, 32(7):1366-1344.
[20] DONCHYTS G, SCHELLEKENS J, WINSEMIUS H, et al. A 30 m Resolution Surface Water Mask Including Estimation of Positional and Thematic Differences Using Landsat 8, SRTM and OpenStreetMap:A Case Study in the Murray-Darling Basin, Australia[J]. Remote Sensing, 2016, 8(5):386.
[21] 吴志杰, 赵书河. 基于TM图像的"增强的指数型建筑用地指数"研究[J]. 国土资源遥感, 2012, 24(2):50-55. WU Zhijie, ZHAO Shuhe. A Study of Enhanced Index-based Built-up Index Based on Landsat TM Imagery[J]. Remote Sensing for Land & Resources, 2012, 24(2):50-55.

卫星遥感地表温度降尺度的光谱归一化指数法

Spatial Downscaling Research of Satellite Land Surface Temperature Based on Spectral Normalization Index

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 12

编辑推荐

Metrics

本文评价

[1]	朱建军, 付海强, 汪长城. 极化干涉SAR地表覆盖层“穿透测绘”技术进展[J]. 测绘学报, 2022, 51(6): 983-995.
[2]	张文渊, 张书毕, 郑南山, 丁楠, 刘鑫, 马朋序. GNSS/MODIS信号紧耦合水汽层析算法[J]. 测绘学报, 2021, 50(4): 496-508.
[3]	康顺, 陈军, 彭舒. 顾及栅格空间拓扑关系的地表覆盖时空目标不一致性探测方法[J]. 测绘学报, 2019, 48(6): 767-779.
[4]	刘备, 王勇, 娄泽生, 占伟. CMONOC观测约束下的中国大陆地区MODIS PWV校正[J]. 测绘学报, 2019, 48(10): 1207-1215.
[5]	陈军, 武昊, 李松年. 全球地表覆盖领域服务计算的研究进展——以GlobeLand 30为例[J]. 测绘学报, 2017, 46(10): 1526-1533.
[6]	周晓光, 汪红松, 吴志强. 引入二维交细分类型的地表覆盖矢量数据增量更新[J]. 测绘学报, 2017, 46(1): 114-122.
[7]	李寅超, 李建松. 一种基于对象和快照的混合地表覆盖时空数据存储模型[J]. 测绘学报, 2016, 45(7): 858-865.
[8]	韩杰, 谢勇. GF-1卫星WFV影像间匀色方法[J]. 测绘学报, 2016, 45(12): 1423-1433.
[9]	鲁楠, 张委伟, 陈利军, 李志林, 陈军, 李然, 陈学泓, 张宇硕, 刘吉羽. 顾及城乡差异的大区域人口密度估算——以山东省为例[J]. 测绘学报, 2015, 44(12): 1384-1391.
[10]	陈军, 陈利军, 李然, 廖安平, 彭舒, 鲁楠, 张宇硕. 基于GlobeLand30的全球城乡建设用地空间分布与变化统计分析[J]. 测绘学报, 2015, 44(11): 1181-1188.
[11]	陈军陈晋廖安平曹鑫陈利军陈学泓彭舒韩刚张宏伟何超英武昊陆苗. 全球30m地表覆盖遥感制图的总体技术[J]. 测绘学报, 2014, 43(6): 551-557.
[12]	郭健张继贤张永红曹银璇 . 多时相MODIS影像土地覆盖分类比较研究[J]. 测绘学报, 2009, 38(1): 0-11.